ArticlePloS one2010
GOPred: GO molecular function prediction by combined classifiers.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.NAR genomics and bioinformatics · 2025Article
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.BMC bioinformatics · 2018Article
- UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB.Bioinformatics (Oxford, England) · 2016Article
- Multi-instance multilabel learning with weak-label for predicting protein function in electricigens.BioMed research international · 2015Article
- Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2014Article
- The identification of gut neuroendocrine tumor disease by multiple synchronous transcript analysis in blood.PloS one · 2013Article
- Article
- The use of evolutionary patterns in protein annotation.Current opinion in structural biology · 2012Review
- Protein annotation from protein interaction networks and Gene Ontology.Journal of biomedical informatics · 2011Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.